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The research on the effects of roadway geometry on truck crashes is relatively limited in comparison with predictive models developed for total vehicle crashes. The most common predictive models currently used are Poisson and negative binomial models. This study uses a negative binomial model but applies the full Bayes’ methods for improving model performance. To use Bayes’ methods successfully, a learning process was used to develop a final model, which was then compared with a separate validation data set to verify its accuracy. The data set used for this study is based on rural two-lane collector and arterial horizontal curves in Ohio, comprising 15,390 observations from crash records between 2002 through 2006. Specific areas of interest in this study include the impact of shoulder width, horizontal curve radius, curve length, and other traffic parameters. The final results indicate a significant increase in truck crashes due to both horizontal curvature and passenger vehicle volumes. The final model's predictions were improved compared with the initial model, indicating that the learning process is a viable tool for future crash model development.

The effect of trucks on the level of service is determined by considering passenger car equivalents (PCE) of trucks. The Highway Capacity Manual (HCM) uses a single PCE value for all tucks combined. However, the composition of truck traffic varies from location to location; therefore a single PCE-value for all trucks may not correctly represent the impact of truck traffic at specific locations. Consequently, the Indiana Department of Transportation wanted to develop separate PCE values for single-unit and combination trucks to replace the single value provided in the HCM. Traditionally, equivalent delay and microscopic simulations have been used to estimate PCE values. In order to facilitate the development of site specific PCE values, an alternative PCE-estimation methodology was explored in the present study on the basis of lagging headways measured from field traffic data. The study used data from four locations on a single urban freeway and three different rural freeways in Indiana. Three-stage-least-squares (3SLS) regression techniques were used to generate models that predict lagging headways for passenger cars, single unit trucks, and combination trucks. The estimated PCE values for single-unit and combination truck for basic urban freeways (level terrain) were 1.35 and 1.60, respectively. For rural freeways, the estimated PCE values for single-unit and combination truck were 1.30 and 1.45, respectively. However, due to the lack of sufficient quality data for rural freeways, the estimated PCE values for rural freeways are not recommended for use. As expected, traffic variables such as vehicle flow rates and speed have significant impacts on vehicle headways. The use of separate PCE values can have significant influence on the LOS estimation. This study also explored regional variation of PCE values. The results of the likelihood ratio test indicated that it is appropriate to combine data from similar locations (freeway sections at different geographical locations) for the PCE analysis.